In recent years, deep networks and off-the-shelf CNN features have become the first choice to tackle computer vision tasks. Only a handful of studies have developed benthic marine species recognition methods based on deep learning. Beijbom et al., [1]} trained three and five-channel deep CNNs based on the CIFAR10 LeNet architecture [2]} to improve the classification performance for coral and non-coral species. Reflectance and fluorescence images were registered together to obtain a five-channel image, which improved the classification performance by a significant margin. This was the first reported study to employ training of deep networks (from scratch) for benthic marine species recognition.
